In our proposed method, feature transformation network is similar to the PointNet [1]} model. PointNet can not capture local context at different scales, and hence by including different residual layers and additional mlp layers, we try to extract the missing information from the input data. While taking 3D point clouds as input, we need to consider the three main properties of 3D point clouds that differentiate them from 2D images: 1) unordered set, 2) neighboring points can form meaningful interactions, and 3) transformation invariance.
